KEYWORDS: Detection and tracking algorithms, Target detection, Signal to noise ratio, Data processing, Tunable filters, Computer programming, Data modeling, Motion models, Engineering, Computer simulations
The dynamic programming track before detect (DP-TBD) algorithm, with its high target detection performance, has garnered significant attention. However, as the processing dimension increases, the computational complexity inevitably rises. The high computing load poses challenges for the unoptimized DP-TBD algorithm to meet real-time requirements in engineering applications, considering hardware limitations. Therefore, effective reduction of the calculation load is crucial to enable the application of TBD technology in real-time systems. To address the issue of high computational complexity in the DP-TBD algorithm, this work proposes a DP-TBD algorithm based on the Hungarian assignment algorithm. The core concept of this algorithm involves filtering low-amplitude data through a low-threshold process and then using the Hungarian assignment algorithm to establish data associations between adjacent frames. By filtering out a large amount of low threshold data, the proposed DP-TBD algorithm significantly reduces computational complexity. Simulation results demonstrate that the proposed algorithm exhibits good detection performance even in low signal-to-noise ratios (SNR).
This paper proposes a generalized covariance union(GCU) approach to solve the distributed fusion problem of sensors with different fields of view (FoVs). It uses the fusion results within the intersection of the FoVs (IoF)to estimate the(target positioning) measurement error, and then employs this estimated error to correct the multitarget densities outside the IoF. Compared with the current approach, GCU approach is more robust to the sensor-related measurement error. Simulation experiments verified the effectiveness of the proposed approaches.
Aiming to analyze the influence of earth-atmosphere radiation on imaging characteristics of space object. A scene of space object motion and detection was designed by Satellite Tool Kit (STK) where visible light imagers mounted on geosynchronous earth orbit (GEO)/medium earth orbit (MEO) satellites were treated as observation platform, highly elliptical orbit (HEO) satellite was treated as object. Equivalent magnitude models of space object and earth-atmosphere radiation, and formulation for signal-to-noise ratio (SNR) of space object were derived by adopting infinitesimal method, according to spatial relationship between space object, earth, sun, and observation platform. The variation of equivalent magnitude between object and earth-atmosphere radiation, as well as the SNR were analyzed when tracking detector and gazing detector were arranged on observation platform. Simulation results indicate that the SNR of object on low orbit observation platform is higher than that on high orbit observation platform, the SNR of the former is 1.1 orders of magnitude higher than the latter on average, while the average imaging SNR of the latter is 1.9. Tracking detector’s object SNR is higher than gazing detector, the difference is largest when object enters or leaves detecting field of view, yet it is the smallest when object is close to detecting field of view. Moreover, the value of SNR obtained by simulation provides a guidance for the detection and recognition of space object, as well as a way of reduction of earth-atmosphere radiation.
There is an emerging interest on using background prior in saliency detection. However, these methods fail to locate the position of background accurately. In this paper, a novel saliency detection approach which chooses more precise background regions is proposed. First, in order to pick out the real background from the boundary of the image, the background probability is measured by boundary ratio. Next, according to the geodesic distance to background regions, the edge saliency map and color saliency map are calculated in the Edge and RGB-LAB-XY feature space, respectively. Furthermore, combining the saliency cues by using an energy function, the final saliency map is generated. The proposed model has the following two advantages: the erroneous background removal guarantees the accuracy of background and the detection of objects located at the boundary of image; the energy minimization enable the detected objects to be more complete and edges of targets to be clearer. Comprehensive experiments on two benchmark datasets demonstrate the superiority of the proposed algorithm over the 5 state-of-the-art methods.
Image segmentation is an important application in computer vision. Nowadays, image segmentation of infrared image has not gain as much attention as image segmentation of visible light image. But this application is very useful. For example, searching and tracking targets with infrared search and track system (IRST) has been widely used these days due to its special passive mode. So it can be used as a kind of supplementary equipment for radar. Infrared image segmentation can help computers identify backgrounds of the image, and help it automatically adjust the related parameters for the next work, such as targets recognition or targets detection.
Our work proposed a new image segmentation method for infrared image using histogram of oriented gradients (HOG) feature and kernel extreme learning machine (kernel ELM). HOG are feature descriptors which can be used in computer vision and image processing for the purpose of object detection. In this paper, we extract HOG feature of infrared image, and use this feature as the basis for classification. After having feature, we use kernel extreme learning machine to do the segmentation. Kernel extreme learning machine has shown many excellent characteristics in classification. By testing our algorithm proposed in our paper, we demonstrated that our algorithm is effective and feasible.
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